# coding = utf-8
import cv2
import numpy as np
import matplotlib.pyplot as plt
from skimage import feature as ft
import numpy as np
import matplotlib.pyplot as plt

from skimage.feature import haar_like_feature_coord
from skimage.feature import draw_haar_like_feature

# 计算积分图
def integral(img):
    integ_graph = np.zeros((img.shape[0],img.shape[1]),dtype = np.int32)
    for x in range(img.shape[0]):
        sum_clo = 0
        for y in range(img.shape[1]):
            sum_clo = sum_clo + img[x][y]
            integ_graph[x][y] = integ_graph[x-1][y] + sum_clo;
    return integ_graph

#就算所有需要计算haar特征的区域
def getHaarFeaturesArea(width,height):
    widthLimit = width-1
    heightLimit = height/2-1
    features = []
    for w in range(1,int(widthLimit)):
        for h in range(1,int(heightLimit)):
            wMoveLimit = width - w
            hMoveLimit = height - 2*h
            for x in range(0, wMoveLimit):
                for y in range(0, hMoveLimit):
                    features.append([x, y, w, h])
    return features

#通过积分图特征区域计算haar特征
def calHaarFeatures(integral_graph,features_graph):
    haarFeatures = []
    for num in range(len(features_graph)):
        #计算左面的矩形区局的像素和
        haar1 = integral_graph[features_graph[num][0]][features_graph[num][1]]-\
        integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]] -\
        integral_graph[features_graph[num][0]][features_graph[num][1]+features_graph[num][3]] +\
        integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]+features_graph[num][3]]
        #计算右面的矩形区域的像素和
        haar2 = integral_graph[features_graph[num][0]][features_graph[num][1]+features_graph[num][3]]-\
        integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]+features_graph[num][3]] -\
        integral_graph[features_graph[num][0]][features_graph[num][1]+2*features_graph[num][3]] +\
        integral_graph[features_graph[num][0]+features_graph[num][2]][features_graph[num][1]+2*features_graph[num][3]]
        #右面的像素和减去左面的像素和
        haarFeatures.append(haar2-haar1)
    return haarFeatures

if __name__ == "__main__":
    ones = np.ones((5,5),dtype=np.uint8)

    img = cv2.imread("D:\\data\\img\\ObjectCategories\\bonsai\\image_0001.jpg",0)
    print ("start")

    features = ft.haar_like_feature(img,  # input image
                                    10,  # Row-coordinate of top left corner of the detection window.
                                    10,  # Column-coordinate of top left corner of the detection window.
                                    40,  # Width of the detection window.
                                    40,  # Height of the detection window.
                                    feature_type='type-2-x' # The type of feature to consider:
                                    )
    #integeralGraph = integral(img)
    #featureAreas = getHaarFeaturesArea(img.shape[0],img.shape[1])
    #haarFeatures = calHaarFeatures(integeralGraph,featureAreas)
    print("stop")
    print(features.shape[0])